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Image processing and machine learning for fully automated probabilistic evaluation of medical images

Luka Šajn and Matjaž Kukar (2010) Image processing and machine learning for fully automated probabilistic evaluation of medical images. Computer methods and programs in biomedicine . (In Press)

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    The paper presents results of our long-term study on using image processing and data mining methods in a medical imaging. Since evaluation of modern medical images is becoming increasingly complex, advanced analytical and decision support tools are involved in integration of partial diagnostic results. Such partial results, frequently obtained from tests with substantial imperfections, are integrated into ultimate diagnostic conclusion about the probability of disease for a given patient. We study various topics such as improving the predictive power of clinical tests by utilizing pre-test and post-test probabilities, texture representation, multi-resolution feature extraction, feature construction and data mining algorithms that significantly outperform medical practice. Our long-term study reveals three significant milestones. The first improvement was achieved by significantly increasing post-test diagnostic probabilities with respect to expert physicians. The second, even more significant improvement utilizes multi-resolution image parametrization. Machine learning methods in conjunction with the feature subset selection on these parameters significantly improve diagnostic performance. However, further feature construction with the principle component analysis on these features elevates results to an even higher accuracy level that represents the third milestone. With the proposed approach clinical results are significantly improved throughout the study. The most significant result of our study is improvement in the diagnostic power of the whole diagnostic process. Our compound approach aids, but does not replace, the physician’s judgment and may assist in decisions on cost effectiveness of tests.

    Item Type: Article
    Keywords: machine learning, coronary artery disease, medical diagnostics, multi-resolution image parameterization, association rules, principal component analysis
    Related URLs:
    URLURL Type
    http://www.cobiss.si/scripts/cobiss?command=search&base=50070&select=(id=8333652)Alternative location
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Divisions: Faculty of Computer and Information Science > Computer Vision Laboratory
    Item ID: 1146
    Date Deposited: 17 Sep 2010 11:04
    Last Modified: 09 Dec 2013 10:34
    URI: http://eprints.fri.uni-lj.si/id/eprint/1146

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